ARTIFICIAL INTELLIGENCE TRAINING PROGRAMS
“Artificial Intelligence Training Programs” are organized within the framework of cooperation between Ankara University Continuing Education Center (ANKÜSEM) and the Office of the Dean of Research…
PURPOSE
Artificial intelligence is the collective term for studies aimed at enabling computers to acquire human-like abilities such as thinking, interpreting, and reasoning. In this context, the objective is to provide participants with the skills to analyze data, identify relevant problems and determine the most appropriate solution methods, and effectively implement these solutions by utilizing the capabilities offered by artificial intelligence technologies.
TRAINING MODULES, DATES, AND CONTENT
| MODULE 1: INTRODUCTION TO PROGRAMMING (WITH PYTHON) | |
| Hour 1: Introduction to Programming Concepts: What Is It and Why Should We Learn It?
Hour 2: Python Installation and Development Environment Hour 3: Variables and Data Types Hour 4: Basic Operators and Expression Structures Hour 5: Control Structures: Conditions (if-else) Hour 6: Loops (for and while) Hour 7: Functions and Modular Programming Hour 8: List, Tuple, and Dictionary Data Structures Hour 9: File Operations and Data Input/Output Hour 10: Developing a Simple Python Project |
04–08 November 2024 10 hours |
| MODULE 2: DATA STRUCTURES AND ALGORITHMS | |
| Hour 1: Introduction to Data Structures: What Are They and Why Are They Important?
Hour 2: Arrays and List Management Hour 3: Stacks and Queues Hour 4: Linked Lists Hour 5: Trees and Binary Search Trees Hour 6: Graph Structures and Basic Graph Algorithms Hour 7: Sorting Algorithms (Bubble, Merge, Quick) Hour 8: Search Algorithms (Linear, Binary) Hour 9: Recursion and Dynamic Programming Hour 10: Algorithm Analysis and Big-O Notation |
11–15 November 2024 10 hours |
| MODULE 3: INTRODUCTION TO ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING | |
| Hour 1: What Is Artificial Intelligence? History and Application Areas
Hour 2: What Is Machine Learning? Types and Fundamental Concepts Hour 3: Supervised and Unsupervised Learning Hour 4: Basic Statistics and Probability Concepts Hour 5: Python and Artificial Intelligence: Introduction and Core Libraries Hour 6: Data Types and Data Preprocessing Hour 7: Modeling and Algorithm Selection Hour 8: Model Performance Evaluation: Error Metrics Hour 9: First Machine Learning Project: A Simple Classification Problem Hour 10: Ethical Principles in Artificial Intelligence and Future Perspectives |
18–22 November 2024 10 hours |
| MODULE 4: DATA SCIENCE AND DATA ANALYTICS | |
| Hour 1: Introduction to Data Science: Concepts and Processes
Hour 2: Data Collection and Data Quality Hour 3: Data Visualization Techniques and Tools Hour 4: Data Exploration with Exploratory Data Analysis (EDA) Hour 5: Data Cleaning and Transformation Hour 6: Statistical Data Analysis Hour 7: Regression Analysis and Applications Hour 8: Time Series Analysis Hour 9: Dataset-Related Issues and Solutions Hour 10: Real-World Data Science Project |
25–29 November 2024 10 hours |
| MODULE 5: SUPERVISED LEARNING TECHNIQUES | |
| Hour 1: What Is Supervised Learning?
Hour 2: Linear Regression: Fundamentals and Applications Hour 3: Logistic Regression and Classification Hour 4: Decision Trees and Random Forests Hour 5: Support Vector Machines (SVM) Hour 6: Naïve Bayes Classifiers Hour 7: K-Nearest Neighbor (KNN) Algorithm Hour 8: Model Overfitting and Regularization Techniques Hour 9: Model Selection and Hyperparameter Optimization Hour 10: Project: Real-Life Supervised Learning Application |
02–06 December 2024 10 hours |
| MODULE 6: UNSUPERVISED LEARNING AND CLUSTERING TECHNIQUES | |
| Hour 1: Introduction to Unsupervised Learning
Hour 2: What Is Clustering? Fundamental Concepts Hour 3: K-Means Clustering Hour 4: Hierarchical Clustering Hour 5: Density-Based Clustering (DBSCAN) Hour 6: Dimensionality Reduction Techniques: PCA and t-SNE Hour 7: Anomaly Detection Hour 8: Evaluation of Clustering Results Hour 9: Mixture Models and Applications Hour 10: Project: Data Segmentation with Unsupervised Learning |
09–13 December 2024 10 hours |
| MODULE 7: DEEP LEARNING AND NEURAL NETWORKS | |
| Hour 1: Introduction to Neural Networks: Structure and Functioning
Hour 2: Feedforward Neural Networks and Activation Functions Hour 3: Backpropagation and Optimization Techniques Hour 4: Deep Neural Networks (DNN) and Training Process Hour 5: Convolutional Neural Networks (CNN) and Image Processing Hour 6: Sequential Data Processing with RNN and LSTM Models Hour 7: Transfer Learning and Pre-trained Models Hour 8: Autoencoders and Generative Adversarial Networks (GANs) Hour 9: Model Reliability and Common Challenges Hour 10: Project: Image Recognition Application with Deep Learning |
16–20 December 2024 10 hours |
| MODULE 8: NATURAL LANGUAGE PROCESSING (NLP) | |
| Hour 1: Introduction to NLP: Language Models and Application Areas
Hour 2: Text Preprocessing Techniques Hour 3: Tokenization and Word Distribution Hour 4: Word Embedding Techniques: Word2Vec, GloVe Hour 5: Language Modeling with RNN and LSTM Hour 6: Attention Mechanism and Transformer Models Hour 7: NLP with BERT and GPT Models Hour 8: Sentiment Analysis and Applications Hour 9: Natural Language Understanding and Translation Systems Hour 10: Project: Text Classification with Natural Language Processing |
23–27 December 2024 10 hours |
| MODULE 9: INTRODUCTION TO REINFORCEMENT LEARNING | |
| Hour 1: Introduction to Reinforcement Learning: Fundamental Concepts and Terminology
Hour 2: Markov Decision Processes (MDP) Hour 3: Policies and Value Functions Hour 4: Dynamic Programming and the Bellman Equation Hour 5: Monte Carlo Methods Hour 6: Temporal Difference (TD) Learning Hour 7: Q-Learning Algorithm Hour 8: Deep Reinforcement Learning Hour 9: Policy-Based Methods Hour 10: Applied Project: A Simple Reinforcement Learning Simulation |
30 December – 03 January 2024 10 hours |
| MODULE 10: AI PROJECT MANAGEMENT AND APPLICATIONS | |
| Hour 1: Introduction to Artificial Intelligence Projects
Hour 2: Fundamentals of Project Management and Applications for AI Projects Hour 3: Data and Model Management Hour 4: Model Deployment and Integration Hour 5: DevOps and MLOps for AI Applications Hour 6: Team Management in AI Projects Hour 7: Real-World AI Application Examples Hour 8: Risk Management in AI Projects Hour 9: AI Project Success Criteria and KPIs Hour 10: Certificate Program Final Project: Comprehensive AI Application |
06 – 10 January 2024 10 hours |
DURATION
Each module consists of 10 hours.
TRAINING DATES AND HOURS
04 November 2024 – 10 January 2025
Weekdays: 18:00–20:00
VENUE
Online (Distance Learning)
The training will commence once a sufficient number of participants is reached.
CERTIFICATE TO BE AWARDED
At the end of each 10-hour module, participants who fulfill the 80% attendance requirement will be entitled to receive a “Certificate of Participation.” In addition, those who attend six or more modules and successfully pass the theoretical examination will be entitled to receive an “Artificial Intelligence Training Certificate.”
FEE
4,000 TL
Course Features
- Lecture 0
- Quiz 0
- Duration 10 weeks
- Skill level All levels
- Language Türkçe
- Students 0
- Assessments Yes
